Analysis
Here are basic counts for how many beers we have in the final data by both broad style and substyle. LOTS of IPAs, although that shoudl surprise no one :) The top four substyles are all IPAs or American pales.
top_beers3 %>%
count(broad_style, sort = TRUE) %>%
head(20)## # A tibble: 20 x 2
## broad_style n
## <chr> <int>
## 1 IPA 12624
## 2 Stout 5602
## 3 Lager 4080
## 4 Pale Ale 3618
## 5 Wheat Beer 2230
## 6 Farmhouse Ale 2023
## 7 Porter 2011
## 8 Wild Ale 1588
## 9 Red Ale 1486
## 10 Pilsner 1324
## 11 Sour 1301
## 12 Strong Ale 1100
## 13 Brown Ale 1097
## 14 Fruit and Field Beer 961
## 15 Blonde Ale 869
## 16 Bock 661
## 17 Barleywine 603
## 18 Bitter 559
## 19 Tripel 506
## 20 Kölsch 434top_beers3 %>%
count(beer_style, sort = TRUE) %>%
head(20)## # A tibble: 20 x 2
## beer_style n
## <chr> <int>
## 1 IPA - American 5050
## 2 IPA - Imperial 3912
## 3 Pale Ale - American 2758
## 4 IPA - New England 2456
## 5 Stout - American Imperial 2076
## 6 Farmhouse Ale - Saison 1867
## 7 Wild Ale 1588
## 8 Red Ale - American Amber / Red 1139
## 9 Porter - American 1064
## 10 Fruit and Field Beer 961
## 11 Stout - American 894
## 12 Stout - Sweet / Milk 808
## 13 Stout - Russian Imperial 807
## 14 Pilsner - German 775
## 15 Blonde Ale - American 754
## 16 Wheat Beer - Hefeweizen 705
## 17 Wheat Beer - Witbier 688
## 18 Brown Ale - American 662
## 19 Sour - Berliner Weisse 657
## 20 Wheat Beer - American Pale 602Comparing styles
First, let’s take a look at the raw number and percent of each style that is either rated above a 4 by BA users or above a 90 in the BA score. Above a 90 indicates that a beer is either in the “Outstanding” or “World Class” categories.
## num/percent of beers > 4 rating
top_beers3 %>%
group_by(broad_style) %>%
summarize(n = n(),
n_over4 = sum(avg >= 4, na.rm = TRUE),
percent_over4 = round((n_over4 / n),2)) %>%
filter(!is.na(broad_style)) %>%
ggplot(aes(percent_over4, reorder(broad_style, percent_over4), fill = broad_style)) +
geom_col() +
theme_classic() +
theme(legend.position = "none",
text = element_text(size=9)) +
labs(y = "Beer Style", x = "Percent of Beers with an Average Rating >= 4",
title = "Percent of Beers with an Average Rating >= 4") +
scale_x_continuous(labels = scales::percent_format()) 
top_beers3 %>%
group_by(broad_style) %>%
summarize(n = n(),
n_over4 = sum(avg >= 4, na.rm = TRUE),
percent_over4 = round((n_over4 / n),2)) %>%
filter(!is.na(broad_style)) %>%
ggplot(aes(n_over4, reorder(broad_style, n_over4), fill = broad_style)) +
geom_col() +
theme_classic() +
theme(legend.position = "none",
text = element_text(size=9)) +
labs(y = "Beer Style", x = "Number of Beers with an Average Rating >= 4",
title = "Number of Beers with an Average Rating >= 4")
top_beers3 %>%
group_by(broad_style) %>%
summarize(n = n(),
n_over90 = sum(score >= 90, na.rm = TRUE),
percent_over90 = round((n_over90 / n),2)) %>%
filter(!is.na(broad_style)) %>%
ggplot(aes(percent_over90, reorder(broad_style, percent_over90), fill = broad_style)) +
geom_col() +
theme_classic() +
theme(legend.position = "none",
text = element_text(size=9)) +
labs(y = "Beer Style", x = "Percent of Beers with a BA Score >= 90",
title = "Percent of Beers by General Style with a BA Score >= 90") +
scale_x_continuous(labels = scales::percent_format())
Wild ales and lambics do well just about however you slice the data. Obviously the list of lambic brewers and blenders in the world is small, but it’s still remarkable to have over 60% of a style rated as either outstanding or world class.
The fact that the single highest number of amazing (4+ by reader ratings) IPAs speaks to the insane number of IPAs produced right now. And it’s no surprise that stouts and wild ales are the second and (distant) third-place styles – those are usually the kinds of beers we think of as hype whales.
Also of note: based on reader averages, high-ABV styles like quads, (many) stouts, barleywines, and (some) IPAs are at or near the top of the list of the most 4+ rated beers. Again, these are whale-ish beers, like Dark Lord, which costs $170 for just five bottles, Hunahpu or Pliny the Younger.
What about the distribution of reviews within a style? Using boxplots and the interquartile range, we can take a look at the most and least-divisive beer styles. The idea here is that beer ratings for some styles are more clustered together than others, while some styles have both very high and very low-rated beers.
## distribution of reviews
top_beers3 %>%
filter(!is.na(broad_style)) %>%
ggplot(aes(avg, reorder(broad_style, avg), color = broad_style)) +
#geom_jitter(aes(alpha = 0.3)) +
geom_boxplot() +
theme_classic() +
theme(legend.position = "none",
text = element_text(size=9)) +
labs(y = "Beer Style", x = "Avg Rating")
## Most divisive styles (within-style IQR)
top_beers3 %>%
group_by(broad_style) %>%
summarize(iqr = IQR(avg)) %>%
filter(!is.na(broad_style)) %>%
arrange(desc(iqr)) %>%
slice_max(order_by = iqr, n = 20) %>%
ggplot(aes(iqr, reorder(broad_style, iqr), color = broad_style)) +
geom_point() +
geom_segment(aes(x = 0, xend = iqr, y = broad_style, yend = broad_style)) +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none",
text = element_text(size=9)) +
labs(x = "Interquartile range", y = "Beer Style",
title = "Most Divisive Beer Styles")
## Most divisive substyles (within-style IQR)
top_beers3 %>%
group_by(beer_style) %>%
summarize(iqr = IQR(avg)) %>%
filter(!is.na(beer_style)) %>%
arrange(desc(iqr)) %>%
slice_max(order_by = iqr, n = 20) %>%
ggplot(aes(iqr, reorder(beer_style, iqr), color = beer_style)) +
geom_point() +
geom_segment(aes(x = 0, xend = iqr, y = beer_style, yend = beer_style)) +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=9),
legend.position = "none") +
labs(x = "Interquartile range", y = "Beer substyle",
title = "Most Divisive Beer Substyles")
# Least divisive substyles (within-style IQR)
top_beers3 %>%
group_by(beer_style) %>%
summarize(iqr = IQR(avg)) %>%
filter(!is.na(beer_style)) %>%
arrange(desc(iqr)) %>%
slice_min(order_by = iqr, n = 20) %>%
ggplot(aes(iqr, reorder(beer_style, -iqr), color = beer_style)) +
geom_point() +
geom_segment(aes(x = 0, xend = iqr, y = beer_style, yend = beer_style)) +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=9),
legend.position = "none") +
labs(x = "Interquartile range", y = "Beer substyle",
title = "Least Divisive Beer Substyles") 
Some thoughts here:
- The median wild ale is likely to be pretty good. These beers, like quads, lambics and brett beers, are difficult and/or take a long time to produce. Essentially, the brewer usually has to be pretty accomplished to make and sell one of these styles, so it’s likely to be pretty good. And of course, those long production times and large amounts of raw materials mean that these beers are often more expensive than most. So, since these are consumer reviews, it’s likely that drinkers either somewhat, if unconsciously, inflate their ratings to justify the cost, or just have a higher perceived enjoyment of the beer just because the beer style and marketing signal that it should be a good beer.
- Light lagers like Bud and High Life are among the most divisive beers. Based on a previous analysis it looks like some of this variation can be explained by joke reviews. Other kinds of lagers are also divisive, including European dark lagers (like Dunkels, Schwarzbiers, and Tmave Pivos), Dortmunders, and Japanese rice lagers. I think this may be partly because even some of the best lagers have less intense flavors than something like an IPA, and partly because lagers as a broad category often get lumped together with light lagers.
- Other divisive styles, like Kvass, Lambic, and Gueuze, may be much different than anything a drinker has ever had before. They’re often sour, smell funky (with descriptors like “horse blanket” and “barnyard”), and are rare, so they’re unlike most other beer styles that people have tried before.
- But interestingly, some of those similarly-rare styles like smoked porter, braggot, brett beer, and Biere de Garde are among the least-divisive styles. In part I think this is because these styles are comparatively rare, so the sample size of available beers is lower. And I’d argue that unlike Lambic/Gueuze, they’re rare enough that many people might not get randomly offered one at some point, like a Lambic. For example, fruited Lambics are available at a lot of fancy grocery stores, and I wouldn’t think it would be uncommon for a beer geek friend to share a gueuze with people who aren’t as in to beer, because of the style’s reputation and because they’re nice for special occasions. Basically, for the least-divisive styles, the kinds of people that usually review them on Beer Advocate are also their biggest fans. You don’t typically just stumble onto a brett beer sight unseen.
The relationship between ABV and ratings
As noted above, high-ABV beer styles are often highly-rated, but what about higher-ABV beers within a style?
## ABV vs. ratings (broad style and beer style)
top_styles <- top_beers3 %>%
count(broad_style, sort = TRUE) %>%
top_n(9)
top_beers3 %>%
filter(broad_style %in% top_styles$broad_style,
abv < 20) %>%
ggplot(aes(abv, avg)) +
geom_point(aes(alpha = 0.4, color = broad_style)) +
geom_smooth() +
facet_wrap(~broad_style, scales = "free_x") +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none") +
labs(y = "Avg Rating", x = "ABV", title = "ABV vs. Avg User Rating by General Style")
top_styles <- top_beers3 %>%
count(beer_style, sort = TRUE) %>%
top_n(9)
top_beers3 %>%
filter(abv < 20,
beer_style %in% top_styles$beer_style) %>%
ggplot(aes(abv, avg)) +
geom_point(aes(alpha = 0.4, color = beer_style)) +
geom_smooth() +
facet_wrap(~beer_style, scales = "free_x") +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none") +
labs(y = "Avg Rating", x = "ABV", title = "ABV vs. Avg User Rating by Substyle")
In general, for most of the top-9 styles, there’s not a strong relationship between ABV and average BA user rating. However, for stouts, wheat beers, and porters, there is a small positive correlation. Interestingly, for substyles, there’s not as strong of a relationship between ABV and rating for double IPAs as for imperial stouts.
Top breweries by style
We can also look at the top breweries by style. Here I use mean rating by style for brewers that have at least two beers with 10+ reviews on Beer Advocate.
## top breweries by style (brewery has to have 3 or more examples)
library(tidytext)
top_styles <- top_beers3 %>%
count(broad_style, sort = TRUE) %>%
top_n(9)
top_beers3 %>%
group_by(broad_style, brewery) %>%
summarize(n = n(),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
filter(n > 2,
!is.na(broad_style),
broad_style %in% top_styles$broad_style) %>%
group_by(broad_style) %>%
slice_max(n = 10, order_by = avg_rating) %>%
ggplot(aes(avg_rating, reorder_within(brewery, avg_rating, broad_style),
color = broad_style)) +
geom_point() +
geom_segment(aes(x = 0, xend = avg_rating,
y = reorder_within(brewery, avg_rating, broad_style),
yend = reorder_within(brewery, avg_rating, broad_style))) +
facet_wrap(~broad_style, scales = "free_y") +
scale_y_reordered() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=9),
legend.position = "none") +
labs(x = "Average Rating", y = "Brewery", title = "Top Breweries by Style")
top_styles <- top_beers3 %>%
count(beer_style, sort = TRUE) %>%
top_n(9)
top_beers3 %>%
group_by(beer_style, brewery) %>%
summarize(n = n(),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
filter(n > 2,
!is.na(beer_style),
beer_style %in% top_styles$beer_style) %>%
group_by(beer_style) %>%
slice_max(n = 10, order_by = avg_rating) %>%
ggplot(aes(avg_rating, reorder_within(brewery, avg_rating, beer_style),
color = beer_style)) +
geom_point() +
geom_segment(aes(x = 0, xend = avg_rating,
y = reorder_within(brewery, avg_rating, beer_style),
yend = reorder_within(brewery, avg_rating, beer_style))) +
facet_wrap(~beer_style, scales = "free_y") +
scale_y_reordered() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=7),
legend.position = "none") +
labs(x = "Average Rating", y = "Brewery", title = "Top Breweries by Substyle")
Top brewers in the country
To me, one of the signs of a truly amazing brewer is that you can buy any beer they make, no matter the style, and have confidence that it’ll be a great beer. Creature Comforts, which is (well, was, when I lived in Athens) local to me, is one of those breweries. Sierra Nevada is another.
So, the following charts look at breweries that have top-20-rated beers in multiple styles. This is just one way to look at best breweries, but I think the results track pretty closely with who you’d expect.
## (number of styles that a brewery has a top 20 avg rating in)
top_beers3 %>%
group_by(broad_style, brewery) %>%
summarize(n = n(),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
filter(n > 2) %>%
group_by(broad_style) %>%
slice_max(order_by = avg_rating, n = 20) %>%
ungroup() %>%
count(brewery, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>%
ggplot(aes(n, reorder(brewery,n), fill = brewery)) +
geom_col() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=10),
legend.position = "none") +
labs(x = "Number of Styles w/ Top 20 Avg Rating", y = "Brewery",
title = "Breweries That Are Awesome at Multiple Styles (Avg Rating")
## By score > Rating
top_beers3 %>%
group_by(broad_style, brewery) %>%
summarize(n = n(),
avg_rating = round(mean(score, na.rm = TRUE), 2)) %>%
filter(n > 2) %>%
group_by(broad_style) %>%
slice_max(order_by = avg_rating, n = 20) %>%
ungroup() %>%
count(brewery, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>%
ggplot(aes(n, reorder(brewery,n), fill = brewery)) +
geom_col() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=10),
legend.position = "none") +
labs(x = "Number of Styles w/ Top 20 Avg Score", y = "Brewery",
title = "Breweries That Are Awesome at Multiple Styles (Avg Score")
top_beers3 %>%
group_by(beer_style, brewery) %>%
summarize(n = n(),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
filter(n > 2) %>%
group_by(beer_style) %>%
slice_max(order_by = avg_rating, n = 10) %>%
ungroup() %>%
count(brewery, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>%
ggplot(aes(n, reorder(brewery,n), fill = brewery)) +
geom_col() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
text = element_text(size=10),
legend.position = "none") +
labs(x = "Number of Beer Substyles w/ Top 20 Avg Rating", y = "Brewery",
title = "Breweries That Are Awesome at Multiple Substyles")
Trillium, Jackie O’s, Hill Farmstead, Tree House, Suarez, Sante Adairius, Holy Mountain, Side Project… these are all who you’d probably shortlist for the best breweries in the country.
## relationship bet score and avg rating
top_styles <- top_beers3 %>%
count(broad_style, sort = TRUE) %>%
top_n(9)
top_beers3 %>%
filter(broad_style %in% top_styles$broad_style) %>%
ggplot(aes(x = score, y = avg, color = broad_style, alpha = 0.4)) +
geom_point() +
theme_classic() +
theme(panel.grid.major.y = element_blank(),
panel.border = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none") +
facet_wrap(~ broad_style, scales = "free") +
labs(x = "Beer Advocate Score", y = "Avg User Rating",
title = "Comparison Between Beer Advocate Scores and User Ratings")
Best breweries by state / state specialties
Beervana’s Jeff Alworth often likes to talk about beer being part of, and a reflection of, culture – and as a result, places develop local and regional specialties.
Even with large regional craft breweries like New Belgium or Boston Beer, states still develop local specialties – like IPAs and Pilsners in Oregon and NE IPAs in Vermont and Massachusetts.
So let’s make some maps. First we’ll make a basemap of the lower 48 states using the tigris package.
library(tigris)
library(sf)
library(leaflet)
us <- states(cb = TRUE, progress_bar = FALSE)
drop_states <- c("Commonwealth of the Northern Mariana Islands",
"United States Virgin Islands",
"Alaska", "Hawaii", "American Samoa", "Guam", "Puerto Rico")
us2 <- us %>%
filter(!NAME %in% drop_states) %>%
st_set_crs("4326")Then we can find the top states per style – we’ll look at states with the most beers in a style (for the top 9 styles) either rated over 4 by users or over 90 by the BA score.
## top states per style
top_styles <- top_beers3 %>%
count(broad_style, sort = TRUE) %>%
top_n(9)
beer_state_styles <- top_beers3 %>%
group_by(broad_style, state) %>%
summarize(n_beers = n(),
n_over4 = sum(avg >= 4, na.rm = TRUE),
n_over90 = sum(score >= 90, na.rm = TRUE),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
filter(broad_style %in% top_styles$broad_style) %>%
right_join(us2, by = c("state" = "NAME")) %>%
st_sf(sf_column_name = "geometry", crs = 4326)
maps <- map(top_styles$broad_style, ~ ggplot(beer_state_styles) +
geom_sf() +
geom_sf(data = beer_state_styles %>% filter(broad_style == .x),
aes(fill = n_over4)) +
scale_fill_viridis_b(direction = -1) +
theme_void() +
ggtitle(.x))
wrap_plots(maps)
maps2 <- map(top_styles$broad_style, ~ ggplot(beer_state_styles) +
geom_sf() +
geom_sf(data = beer_state_styles %>% filter(broad_style == .x),
aes(fill = n_over90)) +
scale_fill_viridis_b(direction = -1) +
theme_void() +
ggtitle(.x))
wrap_plots(maps2)
States With the Most Beers with an Avg Rating > 4
States With the Most Beers with a BA Score > 90
It’s not a surprise that California is an IPA hotbed… but what about New York? And the lack of Oregon or Washington? For Oregon, I’m guessing it has to be because there are a bunch of highly-rated IPAs, but they don’t have more than 10 reviews, particularly because breweries are incentivized to constantly churn out new one-off beers (in 4-packs of 16oz cans with sticker labels, of course). Or – maybe Beer Advocate just isn’t as big of a thing in Oregon compared to other places like New York. shruggie emoji
Oregon’s awesome lager and wild/farmhouse ale breweries (get you a brewery – pFriem – that can do both IMO..) do come through in some of the other maps, though.
Also, note Texas coming through in farmhouse ales – that’s almost 100% just Jester King!
Another funny note – Florida is particularly strong in porters and stouts – not beers you’d necessarily associate with warm weather and the beach, but breweries like Angry Chair, J. Wakefield Brewing, Cigar City, and Funky Buddha all have amazing dark beers.
Style-specific interactive maps
We can also make an interactive map for the whole country for a single style. Where would you go to find the best pilsners?
Note that the map has hover-over tooltips that show the number of beers with scores > 90 and ratings > 4, as well as average ratings and the top beer in that style in the state.
## leaflet map - Pilsners
make_leaflet <- function(style) {
style_scores <- top_beers3 %>%
filter(broad_style == style) %>%
group_by(state) %>%
summarize(n_beers = n(),
n_over4 = sum(avg >= 4, na.rm = TRUE),
n_over90 = sum(score >= 90, na.rm = TRUE),
avg_rating = round(mean(avg, na.rm = TRUE), 2)) %>%
left_join(top_beers3 %>%
filter(broad_style == style) %>%
group_by(state) %>%
slice_max(order_by = avg, n = 1) %>%
mutate(name = paste(brewery, name, sep = ": ")) %>%
select(top_beer = name)) %>%
right_join(us2, by = c("state" = "NAME")) %>%
st_sf(sf_column_name = "geometry", crs = 4326)
pal <- colorBin("viridis", style_scores$n_over90, pretty = TRUE, reverse = TRUE)
leaflet(style_scores) %>%
addProviderTiles(providers$Stamen.TonerLite,
options = providerTileOptions(minZoom = 2, maxZoom = 5)) %>%
addPolygons(fillColor = ~ pal(n_over90),
weight = 0.5, opacity = 1,
color = "black",
fillOpacity = 0.5, smoothFactor = 0.5,
label = paste0("N reviews > 4 = ", style_scores$n_over4, ", ",
"N scores > 90 = ", style_scores$n_over90, ", ",
"Avg Rating = ", style_scores$avg_rating, ", ",
"Top beer: ", style_scores$top_beer)) %>%
setView(-98.5795, 39.8282, zoom=3)
}
make_leaflet("Pilsner")Or the best state for an IPA?
make_leaflet("IPA")